To deal with the crowd supervision issue of low accuracy and poor real-time performance in public places,an abnormal crowd behavior detection approach based on motion saliency map was proposed.Firstly,the Lucas-Kanade method was used to calculate the optical flow field of the sparse feature points,then the movement direction,velocity and acceleration of feature points were computed after filtering the optical flow field both in time and space.In order to precisely describe the crowd behavior,the amplitude of velocity,the direction change,and the amplitude of acceleration were mapped to three image channels corresponding to R,G,and B,respectively,and the motion saliency map for describing the characteristics of crowd movement was fused by this way.Finally,a convolution neural network model was designed and trained for the motion saliency map of crowd movement,and the trained model was used to detect abnormal crowd behaviors.The experimental results show that the proposed approach can effectively detect abnormal crowd behaviors in real time,and the detection rate in the datasets of UMN and PETS2009 are more than 97.9％.%针对公共场所中人群监控准确性和实时性低的问题,提出一种基于运动显著图的人群异常行为检测方法.该方法首先利用Lucas-Kanade法计算稀疏特征点的光流场,并对光流场进行时间和空间上的滤波处理,然后计算特征点的运动方向、速度和加速度.为了准确描述人群行为,将人群的速度幅值、运动方向变化量和加速度幅值分别映射为图像的R、G、B三个通道,并以此合成代表人群运动特征的运动显著图.最后,设计和训练面向人群运动显著图的卷积神经网络模型,并利用该模型检测人群中是否存在异常行为.实验结果表明,该方法能够有效、实时地检测人群异常行为,在UMN和PETS2009数据集的检测率均达到了97.9％以上.
展开▼
